3.1. Where the response variable is not mortality during laboratory experiments involving adult workers

If a response variable to be measured (e.g. a
phenotype of interest that may change with treatment) is quantitative or
qualitative (i.e.diseased versus not
diseased), then a generalised linear mixed model (GLMM) can be used to analyse
data in which ‘cage’ is a ‘random effect’ parameter and treatment is a ‘fixed
effect’ parameter (Crawley, 2005; Bolker et
al.,2009). Several fixed and
random effect parameters can be analysed in the same statistical model. If
individuals in two or more experimental cages used in the same treatment group
are drawn from the same colony, then a GLMM with ‘source colony’ as a random
effect parameter should also be used to analyse data. This random effect
accounts for the fact that, within the same treatment, variation between two
cages of honey bees drawn from the same colony may not be the same as variation
between two cages drawn from two separate colonies. This statistical approach
accounts for the problem of pseudoreplication in experimental design. If the
factor ‘cage’ and ‘source colony’ are non-significant, an experimenter may be
tempted to treat individual honey bees from the same cage as independent samples
(i.e. ignore ‘cage’). Logically, however, workers drawn from the same cage are
not truly independent samples and therefore it would inflate the degrees of
freedom to treat individual workers as individual replicates. This point
requires further attention by statisticians. In lieu of an immediate solution
to this statistical issue, an experimenter can consider using a nested
experimental design in which ‘individual honey bee’ is nested within ‘cage’, as
presented above.